Word recognition begins before a reader looks directly at a word, as demonstrated by the parafoveal preview benefit and word skipping. Both low-level form and high-level semantic features can be accessed in parafoveal vision and used to promote reading efficiency. However, words are not recognized in isolation during reading; once a semantic representation is retrieved, it must be integrated with the broader sentence context. One open question about parafoveal processing is whether it is limited to shallow stages of lexico-semantic activation or extends to semantic integration. In the present two-experiment study, we recorded event-related brain potentials in response to a sentence-final word that was presented in foveal or parafoveal vision and was either expected, unexpected, or anomalous in the sentence context. We found that word recognition, indexed by the N400, ensued regardless of perception location whereas identification of the semantic fit of a word in its sentence context, indexed by the late positive component, was only observed for foveally perceived but not parafoveally perceived words. This pattern was not sensitive to task differences that promote different levels of orthographic scrutiny, as manipulated between the two experiments. These findings demonstrate separate roles for parafoveal and foveal processing in reading. Public Significance StatementThere has long been a public interest in the prospect of speed reading, but scientists have repeatedly challenged this possibility, pointing out that there is a tradeoff between reading speed and accuracy. In particular, if a reader skims the text and does not look at all or most of the words (as during "speed reading"), they cannot comprehend the text as well. The present study adds additional support, via neural data, for the idea that looking directly at a word is necessary to perform aspects of the reading process that lead to a higher level of understanding of a word and its context.

High throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors such as RGB cameras, hyperspectral sensors and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterise crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalising between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation and quantitative trait measurement. We emphasise the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

Pangenomes are a rich resource to examine the genomic variation observed within a species or genera, supporting population genetics studies, with applications for the improvement of crop traits. Major crop species such as maize (Zea mays), rice (Oryza sativa), Brassica (Brassica spp.), and soybean (Glycine max) have had pangenomes constructed and released, and this has led to the discovery of valuable genes associated with disease resistance and yield components. However, pangenome data are not available for many less prominent crop species that are currently under-utilised. Despite many under-utilised species being important food sources in regional populations, the scarcity of genomic data for these species hinders their improvement. Here, we assess several under-utilised crops and review the pangenome approaches that could be used to build resources for their improvement. Many of these under-utilised crops are cultivated in arid or semi-arid environments, suggesting that novel genes related to drought tolerance may be identified and used for introgression into related major crop species. In addition, we discuss how previously collected data could be used to enrich pangenome functional analysis in genome-wide association studies (GWAS) based on studies in major crops. Considering the technological advances in genome sequencing, pangenome references for under-utilised species are becoming more obtainable, offering the opportunity to identify novel genes related to agro-morphological traits in these species.

Pangenomes aim to represent the complete repertoire of the genome diversity present within a species or cohort of species, capturing the genomic structural variance between individuals. This genomic information coupled with phenotypic data can be applied to identify genes and alleles involved with abiotic stress tolerance, disease resistance, and other desirable traits. The characterisation of novel structural variants from pangenomes can support genome editing approaches such as Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR associated protein Cas (CRISPR-Cas), providing functional information on gene sequences and new target sites in variant-specific genes with increased efficiency. This review discusses the application of pangenomes in genome editing and crop improvement, focusing on the potential of pangenomes to accurately identify target genes for CRISPR-Cas editing of plant genomes while avoiding adverse off-target effects. We consider the limitations of applying CRISPR-Cas editing with pangenome references and potential solutions to overcome these limitations.

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